StepOut Raises $1.5M for AI Football Analytics

Bengaluru-based sports-tech startup StepOut has raised $1.5 million in a Pre-Series A round led by Rainmatter by Zerodha, with participation from SucSEED Innovation Fund and Misfits Capital. The funding will help StepOut expand internationally, invest deeper in AI and computer vision, and scale its product as it explores moving beyond football into other amateur sports over time.

StepOut’s $1.5 Mn raise and the near-term roadmap

StepOut’s latest $1.5 million Pre-Series A round was led by Rainmatter by Zerodha, and the round also included SucSEED Innovation Fund and Misfits Capital. Rainmatter had previously led StepOut’s seed round in late 2024, signalling continued conviction in the company’s direction and execution.

The company has said the new capital will go toward expanding international operations, increasing investments in AI and computer vision, and scaling overall product capabilities. It also plans to broaden its scope beyond football into amateur sports and other disciplines in the future, which typically requires expanding the underlying vision models, sport-specific event detection, and sport-context performance metrics.

From the perspective of the ai world organisation, this is a useful case study of how applied AI is moving from “nice-to-have analytics” to core infrastructure for talent identification, coaching workflows, and sports performance decision-making. It also fits a larger pattern seen across AI adoption cycles: once model accuracy and usability cross a threshold, expansion shifts from experimentation to scaling across geographies, segments, and adjacent use cases.

As the ai world summit conversations increasingly focus on measurable outcomes, StepOut’s trajectory offers a practical narrative for how AI products translate into repeatable value for organisations that need better decisions under time pressure. This is exactly the kind of real-world deployment story that resonates at ai world organisation events and ai conferences by ai world, where operators and builders compare what is working in production—not only what is promising in demos.

What StepOut builds: AI-powered football performance intelligence

StepOut was founded by Jeet Karmakar and Sayak Ghosh, and it positions itself as an AI-powered football performance and intelligence platform built for player development, match analysis, and scouting. The platform is used across youth, semi-professional, and professional football, which is important because performance tooling often fails when it can’t flex across very different match environments and data quality conditions.

At the product level, StepOut’s offering includes AI-driven match analysis, automated highlights, performance dashboards, live match analytics, and advanced football metrics such as xG, xA, PPDA, and player impact scores. These features matter because they reduce the “analysis gap” between teams with large backroom staff and teams that operate with lean coaching resources, particularly in youth and academy contexts where time and budgets are constrained.

StepOut’s deployments include tournaments like the Dream Sports Championship and elite domestic competitions, indicating that its workflows can function in high-intensity, multi-match environments where turnaround time is critical. In modern football operations, the real differentiator is often not just producing data, but producing it quickly enough to influence the next training cycle, the next opponent plan, or the next selection decision.

For the ai world organisation, the technical foundation described here—AI plus computer vision used to interpret complex real-world video—also mirrors what is happening across industries such as retail, manufacturing, and mobility, where perception systems are being pushed into operational settings. That cross-industry similarity is relevant for ai world summit 2025 / 2026 programming because it helps practitioners transfer lessons (deployment, model monitoring, adoption, change management) from one domain to another.

Traction since the last round: matches, players, revenue and retention

Since its previous funding round, StepOut says it has analysed more than 25,000 matches and tracked more than 150,000 players. It also reports 3x year-on-year revenue growth and a 90% customer renewal rate, which—if sustained—suggests the platform is becoming embedded in day-to-day performance workflows rather than being treated as a short-term experiment.

StepOut currently serves 120 clubs, academies, and federations across 23 countries, giving it a footprint that goes beyond a single-market story and moves into a repeatability story. For many AI startups, multi-country adoption is where product assumptions get stress-tested: leagues differ in filming quality, tactical styles vary, and coaching routines change, all of which can affect how insights are interpreted and acted on.

The client and partner set listed includes clubs and federations such as AFC Ajax, Rayo Vallecano, Bengaluru FC, Hong Kong FC, and the All India Football Federation. StepOut is also running pilots with global clubs including Real Madrid, Chelsea, Fulham, and Espanyol, which, if converted, can become strong proof points for credibility in elite performance environments.

In the ai world organisation community, traction metrics like renewals and repeated usage matter because they signal “implementation success,” not just technical novelty. In other words, this is the kind of adoption pattern worth unpacking at ai world organisation events—what drove stickiness, what reduced churn, and what product decisions made the tool coach-friendly rather than analyst-only.

Why football metrics like xG, xA and PPDA are central to the product story

StepOut’s inclusion of advanced metrics—xG (expected goals), xA (expected assists), PPDA (passes per defensive action), and player impact scores—places it in the modern “decision-support” layer of football rather than basic video storage or simple clip generation. These metrics are popular because they shift analysis away from outcomes alone (goals, shots) toward the quality and repeatability of actions, which is what coaches can influence more reliably over time.

xG and xA, for example, help quantify chance quality and chance creation beyond raw goal/assist numbers, making them useful for development conversations, recruitment decisions, and opposition planning. PPDA is often used to describe pressing intensity and defensive engagement patterns, which can be key for understanding whether a team’s tactical approach is being executed consistently. While the definitions are widely known in football analytics, the operational challenge is producing them in a way that is timely, consistent, and easy for a coaching staff to use—and StepOut’s product is positioned around exactly that workflow.

StepOut’s platform also provides automated highlights and dashboards, which address a practical reality: even when advanced metrics exist, most teams still need video clips to contextualise the “why” behind the numbers. When analytics and clips sit in separate tools, staff time is lost and adoption suffers; integrated experiences often improve usage because they match how coaches think and communicate.

This is also where AI and computer vision become strategically important, because the ability to extract structured events and player movement from video is what makes it possible to scale analysis without expanding manual tagging teams linearly. The sports context is simply one of the clearest examples of a broader AI pattern discussed at the ai world summit: turning unstructured inputs (video, audio, text) into structured signals that support faster decisions.

What this funding signals for sports AI—and why it belongs at AI World Summit 2025 / 2026

StepOut’s funding update is not only about capital; it highlights how sports is becoming a serious applied-AI market where computer vision, real-time analytics, and decision support are converging into mainstream performance operations. The stated plan to deepen investment in AI and computer vision while scaling internationally reinforces that the company sees its moat in product capability and model-driven automation, not just distribution.

For the ai world organisation, stories like this are valuable because they translate AI into outcomes that are easy to understand—better coaching feedback loops, more efficient scouting, improved match preparation, and more transparent player development pathways. They also provide a practical lens for enterprise attendees at the ai world summit, since the same core questions show up everywhere: how to validate model outputs, how to integrate AI into existing workflows, and how to measure ROI beyond vanity metrics.

The AI World Organisation positions itself as a global entity dedicated to fostering innovation and collaboration at the intersection of AI and business, and it highlights activities like summits, workshops, and community events worldwide. That makes sports-tech a natural fit for ai world organisation events, because it sits at the intersection of AI innovation, real-time decision-making, and high-stakes performance culture—an environment that quickly exposes whether a product truly works.

As planning accelerates for ai world summit 2025 / 2026, the broader takeaway is that “AI adoption” is increasingly being defined by vertical success stories like sports performance intelligence, not only by horizontal platform announcements. For readers tracking ai conferences by ai world, StepOut’s progress provides a timely example of how AI startups can expand globally by combining domain expertise, measurable results, and product workflows that teams will actually use.

In that sense, this round can be read as another marker of maturity: investors are backing not just AI ideas, but AI systems that can scale across countries, across customer tiers (youth to pro), and potentially across sports categories over time. That combination—credible results, retained customers, and a roadmap grounded in deeper AI capability—is the exact profile that tends to generate the best on-stage case studies and the most useful off-stage networking discussions at the ai world summit and other ai conferences by ai world.